Embodiments of the invention relate generally to diagnostic imaging and, more particularly, to a system and method for correcting inhomogeneity of spatial intensity in three-dimensional (3D) magnetic resonance (MR) image data.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization,” MZ, may be rotated, or “tipped,” into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals is digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
Acquired MR images are corrupted by slowly varying multiplicative inhomogeneities or nonuniformities in spatial intensity. These nonuniformities are especially severe for 3 T (three tesla) MR images and introduce shading artifacts that decrease image quality and may cause clinical misinterpretation.
Two primary methods are used to correct MR image inhomogeneity. The first is a calibration-based method that uses a low-resolution body coil image for the correction. The quality of the correction depends on the quality of the low-resolution body coil image. For 3 T MR applications, the quality of the correction based on a low-resolution body coil image is generally less than optimum.
The second method is a post-processing step that applies an algorithm for inhomogeneity correction. Various intensity correction algorithms have been devised to correct for shading artifacts. Thus, if the observed or acquired MR image signal is defined in a spatial domain for a voxel location (x,y,z) by the function g(x,y,z) as g(x,y,z)=h(x,y,z)*f(x,y,z)+n(x,y,z), where * represents multiplication, h represents the coil profile function, f represents a corrected function, and n represents imaging noise. However, noise is amplified while solving such a multiplicative image formation model. That is, given the acquired data, g, and the transformation, h, the corrected function, f, is solved for in the presence of noise, n. Known methods address noise either by simply ignoring noise amplification, suppressing noise prior to performing inhomogeneity correction, or using methods similar to a Weiner filter to suppress the amplification of noise. However, with all of these techniques, the areas having the highest inhomogeneity correction also have the highest noise amplification. Further, known methods of inhomogeneity correction can produce unsatisfactory results when applied to 3D image data.
Accordingly, it would be desirable to have a system and method capable of correcting inhomogeneity in 3D MR images while reducing or eliminating noise amplification.